Quantifying the effect of air quality control measures during the 2010 Commonwealth Games at Delhi, India

Beig, G.; Chate, D. M.; Ghude, Sachin D.; Mahajan, Anoop S.; Srinivas, R.; Ali, Kaushar; Sahu, Saroj Kumar; Parkhi, Neha; Surendran, Divya E.; Trimbake, H. K.

doi:10.1016/j.atmosenv.2013.08.012

Cited by 80 publications

(41 citation statements)

References 27 publications

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“…Ghude et al (2013b) estimated that NO X emissions from Indian region are growing at a rate of about 3.8% per year and show substantial heterogeneity. As a consequence, surface O 3 levels are exceeding the safe limit for human health (Jain et al, 2005;Ghude et al, 2009) and agricultural productivity (Mittal et al, 2007;Engardt, 2008;Ghude et al, 2014), and are contributing to a major air quality problem in this region (Beig et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Inter-comparison of different NOX emission inventories and associated variation in simulated surface ozone in Indian region

Jena

Ghude

Beig

et al. 2015

Atmospheric Environment

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Section: Introductionmentioning

confidence: 99%

“…Spatial distribution NO X emissions from Top-Down inventory with (a) inversion for the entire year (present work), (b) one month inversion (January 2005,Ghude et al, 2013) and (c) their difference for the year 2005 (emission unit is 10 11 NO molecules/cm 2 /s).…”

mentioning

confidence: 99%

Inter-comparison of different NOX emission inventories and associated variation in simulated surface ozone in Indian region

Jena

Ghude

Beig

et al. 2015

Atmospheric Environment

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“…Beijing, Shanghai, Baoding, Xingtai, Shijiazhuang (Shao et al 2006;Jiming et al 2007;Kan et al 2012; China names ten most polluted cities 2015; Zhou et al 2015), India: i.e. Delhi, Raipur, Kanpur, Agra (Beig et al 2013;Pant et al 2016), Saudi Arabia: i.e. Riyadh, Dammam (Munir et al 2016), as well as in Europe: Bulgaria (Dimitrovgad, Dolny Voden, Montana), Czech Republic (Havirov, Cesky Tesin, Orlova) or even Italy (Soresina, Settimo Torinese, Brescia) (WHO 2016).…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Kraków inhabitants’ health risk caused by the exposure to inhalation of outdoor air contaminants

Gruszecka‐Kosowska

2016

Stoch Environ Res Risk Assess

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Health risk associated with the exposure to the polluted atmospheric air inhalation was estimated for the residents of Kraków, Poland. The air pollution concentration data were obtained from the air-quality monitoring system of the city in 2007-2016. The carcinogenic risk of the studied subpopulations was not acceptable under the formula of C 6 H 6 [ BaP [ As(PM10) [ Cd(PM10) [ Pb(PM10) [ Ni(PM10). The total carcinogenic risk (Rt) amounted to 3.04E-04 for children, 2.22E-04 for infants, 1.45E-04 for women, and 1.22E-04 for men. The same risk was calculated for the top three locations of the monitoring stations in this respect, within the city of Kraków: Kurdwanów Housing Estate, Nowa Huta district, and Krasińskiego Av. Non-carcinogenic risk in the case of all six monitoring stations and in respect of all the studied subpopulations, resulting from the exposure to PM10 and for NO 2 for all stations in case of children and infants, as well as, for adults at Krasińskiego Av. and Dietla Str. stations was rated medium. For C 6 H 6 in the case of adults, children, and infants the risk was rated low. The total risk (HI) of non-carcinogenic pollution was rated medium and ranged as follows: 6.53 for children, 4.70 for infants, 3.19 for women, and 2.67 for men. That type of risk was decreasing at the station locations as follows: Krasińskiego Av. [ Dietla Str. [ Nowa Huta district [ Kurdwanów Housing Estate [ Złoty Róg Str. [ Piastów Housing Estate.

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“…Observations at these stations were made with the Air Quality Management System (AQMS). The AQMS comprises of US Environmental Protection Agency approved analysers housed inside walkway shelters and have a sampling height of 3 meters above ground level (Beig et al, 2013). Table 1: Details of the locations of in situ ozone monitoring stations used in this study.…”

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confidence: 99%

Evaluation of tropospheric ozone and ozone precursors in simulations from the HTAPII and CCMI model intercomparisons – a focus on the Indian Subcontinent

Hakim¹,

Archer‐Nicholls²,

Beig³

et al. 2018

Preprint

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Abstract. Here we present results from an evaluation of model simulations from the International Hemispheric Transport of Air Pollution Phase II (HTAPII) and Chemistry Climate Model Initiative (CCMI) model inter-comparison projects against a comprehensive series of ground based, aircraft and satellite observations of ozone mixing ratios made at various locations across India. The study focuses on the recent past (observations from 2008&#8211;2013, models from 2008&#8211;2010) as this is most pertinent to understanding the health impacts of ozone. To our understanding this is the most comprehensive evaluation of these models' simulations of ozone across the Indian sub-continent to date. This study highlights some significant successes and challenges that the models face in representing the oxidative chemistry of the region. The multi-model range in area weighted surface ozone over the Indian subcontinent is 37.26&#8211;56.11&#8201;ppb, whilst the population weighted range is 41.38&#8211;57.5&#8201;ppb. When compared against surface observations from the Modelling Atmospheric Pollution and Networking (MAPAN) network of eight semi-urban monitoring sites spread across India, we find that the models tend to simulate higher ozone than that which is observed. However, observations of NOx and CO tend to be much higher than modelled mixing-ratios, suggesting that the underlying emissions used in the models do not characterise these regions accurately and/or that the resolution of the models is not adequate to simulate the photo-chemical environment about these surface observations. Empirical Orthogonal Function (EOF) analysis is used in order to identify the extent to which the models agree with regards to the spatio-temporal distribution of the tropospheric ozone column, derived using OMI-MLS observations. We show that whilst the models agree with the spatial pattern of the first EOF of observed tropospheric ozone column, most of the models simulate a peak in the first EOF seasonal cycle represented by principle component 1, which is later than the observed peak . This suggest a widespread systematic bias in the timing of emissions or some other unknown seasonal process. In addition to evaluating modelled ozone mixing ratios, we explore modelled emissions of NOx, CO, VOCs, and the ozone response to the emissions. We find a high degree of variation in emissions from non-anthropogenic sources (e.g. lightning NOx and biomass burning CO) between models. Total emissions of NOx and CO over India vary more between different models in the same MIP than the same model used in different MIPs, making it impossible to diagnose whether differences in modelled ozone are due to emissions or model processes. We therefore recommend targeted experiments to pinpoint the exact causes of discrepancies between modelled and observed ozone and ozone precursors for this region. To this end, a higher density of long term monitoring sites measuring not only ozone but also ozone precursors including speciated VOCs, located in more rural regions of the Indian sub-continent, would enable improvements in assessing the biases in models run at the resolution found in HTAPII and CCMI.

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Quantifying the effect of air quality control measures during the 2010 Commonwealth Games at Delhi, India

Cited by 80 publications

References 27 publications

Inter-comparison of different NOX emission inventories and associated variation in simulated surface ozone in Indian region

Inter-comparison of different NOX emission inventories and associated variation in simulated surface ozone in Indian region

Assessment of the Kraków inhabitants’ health risk caused by the exposure to inhalation of outdoor air contaminants

Evaluation of tropospheric ozone and ozone precursors in simulations from the HTAPII and CCMI model intercomparisons – a focus on the Indian Subcontinent

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